Class 7 Assignment | Geoprocessing & Spatial Analysis

Overlay Relationships

Source

Concepts & Themes:

This week’s assignment will encompass the following concepts covered in Class 7 lecture & lab:

  • GIS Analysis - Proximity and Overlay
  • Calculating Geometry
  • Assigning Field Vaules
  • Thematic Map Design for Hazard Ranking
  • Joining Tabular data to Feature Data

Class 7, Lab 6 Materials:

Class 7 Readings:

This week’s readings will include 1 section from the Essentials of Geographic Information Systems textbook; further, the supplemental technical readings cover best approaches to choosing and using map projections.

The class 7 quiz will cover only content from the Essentials of Geographic Information Systems textbook as follows:

  • Chapter 5 - Geospatial Data Management

    • Searches and Queries
      • Pages 100 - 112

  • Chapter 8 - Spatial Analysis of Vector Data

    • Single Layer Analysis
      • Pages 147 - 150
    • Multiple Layer Analysis
      • Pages 150 - 158

  • Context Reading (following material will not be quizzed, but helpful to understand assignment context):

  • International Committee of the Red Cross - Explosive Remnants of War

  • For further discussion and terminology related to Toxic Remnants of War, see website.

Assignment Steps:

Assignment Preamble:

  • In this assignment, you will create 1 map deliverable that will thematically summarize a series of overlay relationships of ERW (Explosive Remnants of War) and resources (populations, infrastructure, ect.) requiring risk assessment and protection across Kosovo within district level administrative boundaries.

  • As shown in the following chart, there are a series of 8 progressive steps to achieve the final thematic map deliverable:

Assignment Step Chart

The assignment workflow will feature the following components:

  1. Geoprocessing Techniques
  2. Calculating Feature Geometry
  3. Field Calculator Selections
  4. SQL Assignments
  5. Thematic Mapping Techniques
  • To start, download the C7 Assignment Data. Once unzipped, find the following .gdb structure:

Assignment .gdb structure

Note: not all layers in the .gdb will be utilized for this assignment.

  • Set C7_asgmt_data folder as working file path and folder, and create a new folder inside the C7_asgmt_data folder , name Exports - this folder can hold geoprocessing outputs developed during this assignment. If you do not assign a folder, just be aware of the location output of your geoprocessing as you will need to access those processed outputs throughout the assignment.

  • Connect to data source:

Connect to .gdb

  • Data to QGIS - note, neither farmland nor woodland need to be imported:

Import Point and Polygon Features

  • Data viewed within QGIS map view/canvas:

Data View of Assignment Data

  • Projections Verification:

  • Prior to beginning any geoprocessing with QGIS, it is critical to check that the map projection is consistent across all analysis layers. Your layers should all contain the following CRS parameters. While QGIS sees these specific parameters as ‘user defined’, it is a known projection and coordinate system known as the ETRS 1989 Kosovo Grid. Note that the unit measurement is meters, so all geoprocessing processes will utilize meter as default map unit.

  • QGIS user defined parameters:

  • +proj=tmerc +lat_0=0 +lon_0=21 +k=0.9999 +x_0=7500000 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs

Kosovo Grid Project Definition

  • Geoprocess Step #1 - Proximity Buffers:

  • Create BUFFERS

    • UXOLandmineSites Buffer = 100 meters

    • ClusterBombs Buffer = 500 meters

    • Save all resulting buffer features in the assignment folder outside the .gdb. Name the folder exports.

    • UXOLandmineSites Buffer parameters:

Land Mine Buffers

Note: all buffers created in this assignment can abide by a segment count of 30. Increasing the segment count creates a more precise boundary polyline.

  • Results:

Land Mine Buffer Results

  • ClusterBombSites Buffer parameters:

Cluster Bomb Buffers

  • Results:

Cluster Bomb Buffer Results

  • Geoprocess Step #2 - Union:

  • UNION ClusterBombSites Buffer + UXOLandmineSites Buffer:

Union Parameters

Note: The UNION will result in more than 1 record, which at this stage is acceptable. Before proceeding to the final analysis, it must become just one polygon prior to CLIPPING which will be done in succeeding steps for Dissolve in Geoprocess Step #3:

Union Results

  • Geoprocess Step #3 - DISSOLVE:

  • The UNION can be fully dissovled into one polygon feature through several avenues within QGIS. One foolproof appproach is to make a new field entitled dissolve_C (dissolve calculation) and populate it with the field calculator with a simple number, in this case 1:

Dissolve via an Attribute Column

  • Following the above field creation, proceed to field calculator and populate with integer 1:

Integer Assignment of 1

  • Table result should look similar, each record possessing dissolve_C value 1:

Integer Assignment of 1 - Check Result

  • Utilizing GDAL within GRASS tools from the main menu Toolbox, choose Dissolve Polygons and populate the parameters for Dissolve as follows:

Dissolve Polygons -Result (GDAL Tool)

  • The Dissolve should result in one polygon feature, validate this result before proceeding:

Dissolve Polygons -Result (GDAL Tool)

Note: if you are not able to produce a result from the dissolve process above, try producing a temporary file as an alternative:

Run Tool w/ Temporary Output

  • Geoprocess Step #4 - CLIPPING:

  • At this juncture, the ERW area has been determined and dissolved into one feature, but there is no way to determine where ERW starts and stops within a particular district(s). To accomplish this task Clip will be utilized.

  • Populate the parameters for Clip process as follows:

Clip Tool

  • Verify that the Clip results in breaks in the previous dissolve at district edges as seen below. Also verify that a total of 235 records results in the Clipped layer. Note: the AREA column is from the district layer, transposed into the Clipped attribute table. In succeeding steps, we will use this total sq. area per district to calculate a final % contaminated per district.

Visually Inspect Clipped Features

  • Geoprocess Step #5 - Add Geometry Column:

  • Utilizing the Add Geometry Column function under Vector Tools, create this column. Note: in the following image Clipped has been renamed contaminated_districts to distinquish its content over its geoprocessing function:

Add Geometry Column

  • Review the attribute table result. You should see a new area column created area_1 as well as perimeter. These attributes are the sq. area and perimeter length of the Clipped ERW per district.

Add Geometry Column - Result

  • Tabular Calculations Step #6 - Derive % of contamination per district:

  • As a result of Step #5 above, both AREA for districts as well as area_1 for contaminated sq. area (meters) now exists in the attribute table for contaminated_districts, the result of the Clip geoprocessing. The following steps will create a normalized column for % of each district that is contaminated. This is needed to know the relative levels of contamination across all districts in Kosovo.

  • Use the following expression for % contaminated:

    • "area_1" / "AREA" * 100

  • To start, use Add field to create a new column that will house % contaminated. Name the field/column PCTCNTM:

Create Column for % Contaminated

  • The resulting attribute table should resemble the following:

Resulting Empty Column

  • Utilize the Field calculator to derive % contaminated per districts:

% Calculation in Field Calculator

  • Results should be a value for PCTCNTM for each district record:

% Calculated in New Column

  • Tabular Calculations Step #7 - Determine categorical classifcation ‘level of contamination’:

  • While normalized % contamination now exists in the table, a category of ‘level of contamination’ is a more effective approach for priority mapping. In this step both thresholds and a range will be used to develop levels based on the following criteria:

Level Description Definition
None 0 percent
Low <1.3 percent (up to the median value of contamination)
Medium 1.3-6.5 (between one and five times the median value of contamination)
High >6.5 percent (more than five times the median value of contamination)
  • To start, create a column entitled LVLCONTM via Add field, text type with an length of 10. This will be the column for Level Descriptions:

Level Column

  • Use Select by expression utilizing "PCTCNTM" <1.3 as the first expression for ‘low’

Note: in the image below it is inadvertently >=1.3 which is not ‘low’; use <1.3 instead:

Low Classification

  • In Field calculator, assign ‘Low’ value to LVLCONTM, using update exisitng field, not create a new field option:

Low Classification Assignment

  • Ensure that all selected features are Deselect Features from All Layers following each level assignment. This will ensure that selections are not being added incorrectly to proceeding selections; that is, each selection for the 3 values low, medium and high are separate:

Imgur

  • Following the "PCTCNTM" <1.3 expression and level assignnent for ‘low’, continue to enact ‘medium’ and ‘high’ based on the following expressions:

    • Medium: "PCTCNTM" >=1.3 and "PCTCNTM" <=6.5

    • High: "PCTCNTM" >6.5

  • Tabular Calculations Step #8 - Join contamination ranks table to original district feature in preparation for final thematic mapping:

  • After finishing Step #7, export the feature to exports folder as contamination_table. This features houses the 235 districts that contain various levels of contamination. This tabular data must be joined to the original district feature. The result will be 235 districts with full attributes of both features/tables, and remaining districts with NULL records for contamination, meaning they do not have any contamination and can thus be mapped ‘none’ or ‘no contamination’.

    Export Feature with Calculations Completed in Attribute Table

  • Navigate to the original districts feature in Layer panel, properties > join. Utilize the unique OBJECTID field that is common between contaminated_table and districts and create the join:

Join on OBJECTID

  • Once joined, immediately export results as district_joined - this will finalize the join, making it permanent to the feature. If this is not done, the join is not permanent to the feature:

Export Successful Join

  • Thematic Mapping Step #9 - Thematically map ‘Level of ERW Contamination’:

  • Utilize the LVLCONTM column as input into categorical symbols for thematic mapping. Design your classes so that those districts with high appear dense, concentrated relative to medium and low classes. Make sure to visually segment the districts that have no contamination separate from the classes that do indeed have some level of contamination as its especially important to distinquish none from low.

Thematic Design for Contamination Levels per District

  • Design the final map in print composer giving particular attention to the design of the legend. Ensure that the classes are sequenced logically, using item properties for the legend to fine tune and revise:

Classification based on Categorical Values using a Sequence Approach

Assignment #7 Deliverable:

  • 8.5x11” thematic map PDF titled ‘Levels of ERW Contamination - Districts, Kosovo War’ Like the map examples below, you will map thematically using district boundaries for contamination. No need for scale bar or north arrow - just the map results, thematic legend and data source.

  • Data Source - Mine Action Coordination Centre (MACC)

  • Refer to this week’s map examples also for guidance on the appearance of the map.